تولید همزمان و برنامه ریزی عملیات لجستیک در صنایع غذایی نیمه آماده

The production and logistics operations planning in real-life single- or multi-site semicontinuous food industries is addressed in this work. A discrete/continuous-time mixed integer programming model, based on the definition of families of products, is developed for the problem in question. A remarkable feature of the proposed approach is that in the production planning problem timing and sequencing decisions are taken for product families rather than for products. However, material balances are realized for every specific product, thus permitting the detailed optimization of production, inventory, and transportation costs. Changeovers are also explicitly taken into account and optimized. Moreover, alternative transportation modes are considered for the delivery of final products from production sites to distribution centers. The efficiency and the applicability of the proposed approach is demonstrated by solving to optimality two industrial-size case studies, for an emerging real-life Greek dairy industry.

The operation of flexible plants involves the satisfaction of a number of production requirements placing competing demands on a set of limited resources, such as processing equipment, storage capacity, utilities, and manpower. The problem of efficient resource utilization leads to a class of scheduling problems which have received considerable attention over the past couple of decades. Much of the research effort to date has focussed on the planning and scheduling of production for individual plants situated at a single geographical site and involving a set of batch, semicontinuous or even continuous unit operations. As is well known, this is in itself a complex problem, optimal or even feasible solutions to which are often notoriously difficult to obtain. However, it must also be recognized that production scheduling is only one aspect of the wider problem of supply chain scheduling. For instance, the scheduling of plant maintenance operations, the coordinated planning of the production at a number of distinct geographical locations, and the management of distribution and Supply Chains (SCs), all lead to important scheduling problems that interact strongly with supply chain scheduling at individual plants. It might be expected that large benefits would ensue from coordinated planning across sites, in terms of costs and market effectiveness. Most business processes dictate that a degree of autonomy is required at each manufacturing and distribution site, but pressures to coordinate responses to global demand while minimizing cost imply that simultaneous planning of production and distribution across plants and warehouses should be undertaken. This would result in the most efficient utilization of all resources. A target-setting approach, where central plans set achievable production targets without imposing operational details is compatible with operational details being determined at each site. In general, in most production facilities, the production department is responsible for scheduling the production operations so as to satisfy the production targets provided by the logistics department, which is mainly responsible for the management of inventory levels and the distribution of final products. It is evident that a strong interaction between those departments exists, and therefore their appropriate coordination is vital for the overall SC performance. This coordination is not a simple task since production and logistics departments often strive to satisfy different objectives, a fact that may result in organizational and operational problems. Hence, apparently this coordination becomes extremely complicated when several production facilities (multi-site production case) are involved. In this case, it is essential to implement an efficient communication and the coordination of the production and logistics departments of all production plants (i.e., simultaneous production and logistics planning) in order to ensure the viability of the overall SC and increase the overall competitive advantage of the firm by reducing operating, inventory, and transportation costs and increasing customer service levels. In this work, we are focused on the Food Processing Industry (FPI) sector, one of the most important process industries, that has received little attention in the open literature so far. FPIs acquire agricultural raw materials and then process them before further distribution. Here, processing is defined in a broad sense; ranging from simple packing of fresh products to extensive fermentation or reforming operations. In food SCs, logistics management normally refers to the physical material flows and the inventory of products from the production facilities to Distribution Centers (DCs) or customers. More specifically, we consider the simultaneous detailed production and logistics planning in multi-site multiproduct semicontinuous FPIs. A real-life dairy industry producing yogurt is studied in detail, and a novel linear Mixed Integer Programming (MIP) framework, based on the definition of families of products, is proposed for the production and logistics planning problem in hand. In every planning period, the main decisions to be made are: (i) the optimal assignment and sequencing of products (and families) to processing units, (ii) the produced quantity for each product in each processing site, (iii) the inventory levels for each product, (iv) the assignment of transportation trucks to production sites—DCs, (v) the selection of the available alternative transportation mode, and (vi) the detailed transportation load composition for every truck. The objective is to fully satisfy customer demand at minimum total cost, including production, changeover, inventory and transportation costs. To the best of our knowledge, there is no previous work in the literature presenting an exact method for addressing the challenges of the underlying semicontinuous food processing and logistics planning problem. The novelty of the proposed mathematical formulation lays on the integration of three different modeling approaches (see Fig. 1) and the simultaneous optimization of detailed production and distribution operations. More specifically, we use (i) a discrete-time approach for the calculation of inventories and transported quantities for products at the end of each period in the production and logistics operations planning level, (ii) a continuous-time approach for the sequencing of product families at the production planning level, and (iii) lot-sizing type capacity constraints in the production scheduling level for all products. Furthermore, the proposed MIP framework allows products that belong to the same family to have different: (i) processing rates (e.g., packing rates), (ii) operating costs, (iii) setup times, (iv) inventory costs, (v) transportation costs, and (vi) customer type. Finally, alternative transportation modes are taken into account explicitly, and thus their selection is accordingly optimized. The remainder of the manuscript is organized as follows. Section 2 provides a brief literature review on production and transportation problems, and a detailed literature review regarding production and distribution in FPIs. Afterwards, Section 3 describes the problem in question. Section 4 formally states the problem modeled followed by a description of the modeling approach in Section 5. Then, Section 6 presents the proposed mathematical formulation while the subsequent Section 7 illustrates the applicability of the developed framework in two real-life yogurt production and distribution case studies. Finally, some concluding remarks are drawn and future research lines are given in Section 8.

This work presents a novel MIP framework for the simultaneous detailed production and distribution planning problem of multi-site multiproduct semicontinuous FPIs. Salient features of the proposed modeling framework are: (i) a discrete-time approach for the calculation of inventories and transported quantities for products at the end of each period in the production and logistics operations planning level, (ii) a continuous-time approach for the sequencing of product families at the production planning level, (iii) lot-sizing type capacity constraints in the production scheduling level for all products, and (iv) the consideration of alternative transportation modes. Two industrial case studies from the yogurt production sector are used to illustrate the applicability and efficiency of the proposed MIP model that successfully integrates detailed production and logistics operations. To the best of our knowledge, this is the first work presenting an exact method for addressing the challenges of the underlying detailed semicontinuous food processing and logistics planning problem. The proposed integrated framework delivers value beyond plan feasibility and schedule optimization. It may also serve as a tool for negotiations between the manufacturing and SC departments, allowing them to collaborate more easily to find the best balance between inventory levels, production and distribution efficiency. Furthermore, it can provide the basis to analyze the impact of new production and distribution plans on manufacturing efficiency, inventory levels and demand satisfaction. The proposed approached has been successfully validated and approved by the dairy industry considered, and its further improvement and implementation in the firm is currently on the way. It should be pointed out that in order to further enhance the suggested framework, oscillations in product demands and other operating and financial uncertainty factors must be thoroughly examined. For this reason, further research should be directed towards the incorporation of uncertainty in the current mathematical framework, by applying stochastic programming [7] and/or multi-parametric programming techniques [29], and devising efficient decompositions schemes [14] and [15] to appropriately tackle the complexity of the resulting problem. Concluding, the proposed approach could be used to other dairy industries producing milk, ice-cream, cheese, milk cream or butter. Other potential application of the proposed approach, probably with minor modifications, include the beverage industry, the beer industry, the compressed fruit industry, the dried fruit industry, and packing companies involving fruits, vegetables, frozen fish or meat, etc.